# Wikipedia Reader Navigation: When Synthetic Data Is Enough

arXiv: https://arxiv.org/abs/2201.00812 · HTML: https://mlsanity.com/paper/2201.00812/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2022.

## Built on by (most cited first, 6 of 6)

- [Fleet of Agents: Coordinated Problem Solving with Large Language Models](https://mlsanity.com/paper/2405.06691/) (2024)
- [Entity Insertion in Multilingual Linked Corpora: The Case of Wikipedia](https://mlsanity.com/paper/2410.04254/) (2024)
- [Towards a Taxonomy for the Use of Synthetic Data in Advanced Analytics](https://mlsanity.com/paper/2212.02622/) (2022)
- [Considerations for Multilingual Wikipedia Research](https://mlsanity.com/paper/2204.02483/) (2022)
- [DIVERGE: Diversity-Enhanced RAG for Open-Ended Information Seeking](https://mlsanity.com/paper/2602.00238/) (2026)
- [On the effective transfer of knowledge from English to Hindi Wikipedia](https://mlsanity.com/paper/2412.05708/) (2024)

## Abstract (arXiv)

Every day millions of people read Wikipedia. When navigating the vast space of available topics using hyperlinks, readers describe trajectories on the article network. Understanding these navigation patterns is crucial to better serve readers' needs and address structural biases and knowledge gaps. However, systematic studies of navigation on Wikipedia are hindered by a lack of publicly available data due to the commitment to protect readers' privacy by not storing or sharing potentially sensitive data. In this paper, we ask: How well can Wikipedia readers' navigation be approximated by using publicly available resources, most notably the Wikipedia clickstream data? We systematically quantify the differences between real navigation sequences and synthetic sequences generated from the clickstream data, in 6 analyses across 8 Wikipedia language versions. Overall, we find that the differences between real and synthetic sequences are statistically significant, but with small effect sizes, often well below 10%. This constitutes quantitative evidence for the utility of the Wikipedia clickstream data as a public resource: clickstream data can closely capture reader navigation on Wikipedia and provides a sufficient approximation for most practical downstream applications relying on reader data. More broadly, this study provides an example for how clickstream-like data can generally enable research on user navigation on online platforms while protecting users' privacy.